Finding the Sites with Best Accessibilities to Amenities Qianlu Lin, Chuan Xiao, Muhammad Aamir Cheema and Wei Wang University of New South Wales, Australia.

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Presentation on theme: "Finding the Sites with Best Accessibilities to Amenities Qianlu Lin, Chuan Xiao, Muhammad Aamir Cheema and Wei Wang University of New South Wales, Australia."— Presentation transcript:

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Finding the Sites with Best Accessibilities to Amenities Qianlu Lin, Chuan Xiao, Muhammad Aamir Cheema and Wei Wang University of New South Wales, Australia

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Related Literature  KNN – K Nearest Neighbour Given a query point q and a set of data points I, find k data points in I that are nearest neighbour of q  RNN – Reverse Nearest Neighbour Given a query point q and a set of data points I, find k data points of which q is the nearest neighbour  ANN – All Nearest Neighbour Given a set of query points Q and a set of data points I, find nearest neighbour in I for each query point in Q (Y.Chen, ICDE2007) Efficient evaluation of all-nearest- neighbor queries In solving our problem, we can retrieve ANN in each type and find top k queries 4

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Our Contribution  We introduced the problem of finding the sites with best accessibilities to amenities  We proposed two algorithms to find top-k accessible sites among a set of possible locations  We performed experiments on several real datasets 5

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Baseline Apartment Restaurant Bus Stop Zoo 6 ANN is used to retrieve the nearest neighbour of each query for each type.

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Baseline - Disadvantage  I/O time Query data will be accessed n times, n is the number of types of index objects  Memory usage Need find NN for all the query points Need to maintain a list of nearest neighbours of each type of each query 7

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Conclusion  We proposed two algorithms: Separate tree: creates indexes for different types of points in separate R-trees One tree: indexes all the points in a single R- tree  Both algorithms outperform the baseline algorithm with a speed-up up to 5.7 times  Also, both algorithms only need access the Query tree once, which reduces I/O cost on accessing Query tree 20